Recommender systems typically provide recommendations to users based on a user's defined preferences and interests. Early recommender systems, for example Firefly and GroupLens, provided personalized recommendations of movies, restaurants, newsgroup articles and music to an Internet audience. These early systems generally used statistical algorithms to perform what is called in the literature automated collaborative filtering (ACF). Basically, the algorithms learn to identify groups of people with similar preferences within given domains of interest or genres. As a result, recommender systems are able to provide personalized recommendations, predicting how much an item is likely to appeal to a user based on how others evaluated the item. The more items a user evaluates, the better the system will be able to personalize its recommendations. Recommendations usually consist of numerical ratings input manually by users, but they can also be deduced from user behavior (e.g., time spent reading a document, actions such as printing, saving or deleting a document). A premise of recommender systems is that a user is going to prefer an item that is similar to other items chosen by the user and by other users.
An emerging category of product functionality is skill mining. Skill mining lets a system automatically identify users' skills and expertise based on the observation of the documents they produce (e.g., reports, E-mail or discussion database postings). This information is used to augment the system's information retrieval and knowledge management capabilities by causing it to fulfill a user's request for information with pointers to knowledgeable colleagues in addition to document references. Current systems for skill mining have a major drawback; they are based on individual production of information, without collective means for measuring how “authoritative” a person is in a field. However, several algorithms have been proposed in the recent years attempting to measure the “authority” level of a page or of an author by elaborating on the connections among documents, typically either hyperlinks or co-citations. This category of algorithms is based on the observation that the more a resource is “used” or referred to, the more authoritative it is. An example of such category of algorithms is HITS (Kleinberg, J. M., “Authoritative Sources in a Hyperlinked Environment”, IBM Research Report RJ 10076, May 1997, pp. 1–33).
Knowledge Pump, a Xerox system, provides community-based recommendations by initially allowing users to identify their interests and “experts” in the areas of those interests. Knowledge Pump is then able to push relevant information to the users based on those preferences. This is accomplished by monitoring network traffic to create profiles of the users, including their interests and communities of practice, thus refining the community specifications. For many users, items recommended by experts are given greater weight than items recommended by non-experts. However, identifying experts within a community of interest is not always straightforward. What is needed is a method for identifying experts or authorities in a recommender system.